Prediction of Particulate Matter 2.5 Concentration Using a Deep Learning Model with Time-Frequency Domain Information

Abstract

In recent years, deep learning models have gained significant traction and found extensive applications in the realm of PM2.5 concentration prediction. PM2.5 concentration sequences are rich in frequency information; however, existing PM2.5 concentration prediction models lack the ability to capture the frequency information. Therefore, we propose the Time-frequency domain, Bidirectional Long Short-Term Memory (BiLSTM), and attention (TF-BiLSTM-attention) model. First, the model uses Discrete Cosine Transform (DCT) to convert the time domain information into its corresponding frequency domain representation. Second, it joins the time domain information with the frequency domain information, which enables the model to capture the frequency domain information on top of the original. Simultaneously, incorporating the attention mechanism after BiLSTM enhances the importance of critical time steps. Empirical results underscore the superior predictive performance of our proposed univariate model across all sites, outperforming both the univariate BiLSTM, univariate BiLSTM-attention, and univariate TF-BiLSTM. Meanwhile, for the multivariate model that adds PM2.5 concentration from other sites in the study area as input variables, our proposed model outperforms the prediction of some basic models such as BiLSTM and some hybrid models such as CNN-BiLSTM for all sites.

Keywords

• air quality prediction
• deep learning
• BiLSTM
• DCT
• attention mechanism